The demands of aging infrastructure, e.g., bridges, buildings, etc., require effective methods for structural monitoring and maintenance. Such monitoring is useful for intelligent safety, lifetime, and replacement/repair issues, and is critical to improving maintenance practices, minimizing the cost associated with repair, and ultimately improving public safety. Structural health monitoring (SHM) provides devices and systems for capturing structural response and assessing structural condition for a variety of purposes. For example, the information from an SHM system can be used to fine-tune computational structural models, thereby allowing more accurate prediction of the response due to extreme loading conditions, such as severe earthquakes and strong winds. SHM also can be used to characterize loads in situ, which can allow the detection of unusual loading conditions as well as validate the structure's design. In addition, real-time monitoring systems can measure the response of a structure before, during, and after a natural or man-made disaster. Such measured responses can be used in damage detection algorithms to assess the post-event condition of a structure.
While sensors and data acquisition systems for structural response are generally known, more of an effort in recent years has been directed toward use of this data to assess the current state of a structure. These algorithms take the measured structural response along with varying degrees of information regarding the structural model and the input excitation, and attempt to determine if the structure has sustained measurable changes in its condition. Analyzing the measured data in this way is useful for both periodic structural monitoring to track the state of a structure over time as well as for the assessment of a structure following a strong loading event such as an earthquake. In both cases, the result is the ability to implement evacuation, repair, and retrofit strategies that ultimately improve public safety and reduce the life-cycle cost of the structure.
Gaining a clear understanding of structural behavior to allow a reasonable assessment of its as-built condition requires high-fidelity sensor data to build accurate models. In addition, potentially problematic structural changes, such as corrosion, cracking, buckling, fracture, etc., all occur locally within a structure. Sensors should be in close proximity to the damage to capture the resulting change in response; sensors further from the damage are unlikely to observe measurable changes. To achieve an effective monitoring system that is capable of generating informative structural models and detecting critical structural changes, a dense array of sensors should be deployed. Due to the cost of deployment and the potential for data inundation, such dense instrumentation is not practically realized with traditional wired network monitoring technology.
Traditional wired structural monitoring systems are comprised of a network of sensors distributed throughout a structure. These networks typically rely on a central source of power and data acquisition, and therefore require cables to link the sensors with the power and acquisition hardware hub. Implementing modal analysis or damage detection algorithms with wired systems requires all of the sensed data to be collected at the data acquisition center where it is then processed. For a dense array of sensors sampling at the relatively high rates required for SHM, the result is that an enormous amount of data must be communicated and processed at a single location.
Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. However, full-scale implementation of wireless smart sensor networks has proven difficult due to the lack of sensor boards that can meet the difficult combined demands of sensitivity, accuracy, synchronization, communications, and power management.
SHM research is turning to wireless smart sensor networks (WSSN), which include on-board computation capacity to reduce the amount of communication while providing comparable data. State-of-the-art sensor technology provides wireless smart sensors having wireless communication, onboard computation, relatively low cost, and small size. These features enable the deployment of a dense array of sensors on structures, which can provide useful information and increase the potential of a structural health monitoring system.
Advances in wireless communications and embedded sensing have resulted in updates to traditional wired SHM networks. The majority of the work using wireless sensors for structural monitoring has focused on using the sensors to emulate traditional wired sensor systems. As such systems have required that all data be sent back to a central processing center, the amount of wireless communication needed in the network becomes prohibitive in terms of excessive communication times and the associated power it consumes. For example, a wireless sensor network implemented on the Golden Gate Bridge that generated 20 MB of data (80 seconds of data, sampling at 1000 Hz from 64 sensor nodes) took over 12 hours to complete data communication back to a central location.
Several factors determine the level of success that may be achieved by vibration-based SHM using smart sensors. A stable and reliable smart sensor network is required, which may be obtained through advanced hardware and advanced networking software. Effective data processing techniques should be available to process the data using the onboard computation capabilities of a smart sensor. Nonlimiting example hardware to meet such goals includes the Imote2 Sensor Platform [e.g., as described in MEMSIC, Inc., “IPR2400, Imote2 Wireless Sensor Node,” Andover, Mass. (2010)], which has been shown to be well-suited for such high-data throughput application of a range of data aggregation and SHM algorithms. Another type of sensor system is the Mica2 [MEMSIC, Inc., “MICA2, Wireless Measurement System,” Andover, Mass. (2010)], which is specially focused on lower power, low data throughput applications.
The Imote2 exemplifies state-of-the-art smart sensor platforms. The Imote2 (IPR2400) is a wireless sensor node platform using a low-power PXA271 XScale processor operating at 13-416 MHz and an 802.15.4 radio with a 2.4 GHz antenna. It is a modular stackable platform that can be interfaced to other boards for specific applications, and example boards provide battery and sensor functions.
FIG. 1 shows the top and bottom of an Imote2 board 40, and FIG. 2 shows the combination of an Imote2 with a battery board 44, and antenna 46. A separate sensor board interfaces with the Imote2 because it lacks its own sensing capabilities. A popular conventional sensor board for interfacing with the Imote2 40 is the ITS400 [MEMSIC, Inc., “ITS400, Imote2 Basic Sensor Board,” Andover, Mass. (2010)]. For example, the ITS 400 board 42 includes a three-axes digital output linear accelerometer with a 12-bit ADC, a temperature/humidity sensor, a light sensor, and a four-channel 12-bit ADC.
However, the present inventors have discovered a number of serious drawbacks with the ITS400 sensor board for use in SHM methods, including the fact that the sensor board has only four possible sampling rates and cut-off frequencies, unstable sampling rates that can hinder synchronized sensing, non-stationary fluctuations in the sampling rate, lack of anti-aliasing filters, and the low resolution of the accelerometer with the built-in ADC (about 0.98 mg), which is too coarse for ambient measurements of vibration. These deficiencies make this sensor board ill-suited for SHM.
For example, strong structural excitations, such as (but not limited to) earthquakes and hurricanes, can result in high levels of structural response and are readily captured by sensors with limited measurement resolution. The signal-to-noise (SNR) of such structural responses is high and easily provides unambiguous data because the measurement noise becomes negligible. However, most structural responses that can be measured during routine monitoring are low-level ambient vibration responses. Ambient vibration is generated due to a variety of random excitation sources such as nearby traffic, normal wind-loading, machinery inside the structures, etc. Such ambient vibration data can provide important vibration-based for structural health monitoring. Unfortunately, usually the vibration level resulting from such ambient vibration responses is typically too small to capture with commercially available sensor boards for wireless sensors.
Generally, the Imote2 and other structural health monitoring sensor boards are too limited in the information that they provide. Their sensitivity and reliability are not sufficient to provide warning of typical events that can lead to catastrophic failure. Moreover, known sensor boards do not provide high enough levels of sensitivity and accuracy for ambient measures of infrastructure that are important to define baseline conditions. Also, board-to-board variations in conventionally manufactured sensor boards must be accounted for in networked sensor monitoring, and this further limits the sensitivity and accuracy of structural health monitoring sensors.